code: def test_auto_arima_hyperparameter_grid_default():
      """Tests get_hyperparameter_grid and apply_prophet_model_components_defaults"""
      template = AutoArimaTemplate()
      template.config = template.apply_forecast_config_defaults()
      # model_components is None
      hyperparameter_grid = template.get_hyperparameter_grid()
  
      expected_grid = {
          # Additional parameters
          "estimator__freq": [None],
          # pmdarima fit parameters
          "estimator__start_p": [2],
          "estimator__d": [None],
          "estimator__start_q": [2],
          "estimator__max_p": [5],
          "estimator__max_d": [2],
          "estimator__max_q": [5],
          "estimator__start_P": [1],
          "estimator__D": [None],
          "estimator__start_Q": [1],
          "estimator__max_P": [2],
          "estimator__max_D": [1],
          "estimator__max_Q": [2],
          "estimator__max_order": [5],
          "estimator__m": [1],
          "estimator__seasonal": [True],
          "estimator__stationary": [False],
          "estimator__information_criterion": ["aic"],
          "estimator__alpha": [0.05],
          "estimator__test": ["kpss"],
          "estimator__seasonal_test": ["ocsb"],
          "estimator__stepwise": [True],
          "estimator__n_jobs": [1],
          "estimator__start_params": [None],
          "estimator__trend": [None],
          "estimator__method": ["lbfgs"],
          "estimator__maxiter": [50],
          "estimator__offset_test_args": [None],
          "estimator__seasonal_test_args": [None],
          "estimator__suppress_warnings": [True],
          "estimator__error_action": ["trace"],
          "estimator__trace": [False],
          "estimator__random": [False],
          "estimator__random_state": [None],
          "estimator__n_fits": [10],
          "estimator__out_of_sample_size": [0],
          "estimator__scoring": ["mse"],
          "estimator__scoring_args": [None],
          "estimator__with_intercept": ["auto"],
          # pmdarima predict parameters
          "estimator__return_conf_int": [True],
          "estimator__dynamic": [False],
      }
      assert_equal(actual=hyperparameter_grid, expected=expected_grid)
  
imports: import datetime
  import numpy
  import pandas
  import greykite.common.constants
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.common.testing_utils import generate_df_with_reg_for_tests
  from greykite.framework.constants import COMPUTATION_N_JOBS
  from greykite.framework.constants import CV_REPORT_METRICS_ALL
  from greykite.framework.templates.auto_arima_template import AutoArimaTemplate
  from greykite.framework.templates.autogen.forecast_config import ComputationParam
  from greykite.framework.templates.autogen.forecast_config import EvaluationMetricParam
  from greykite.framework.templates.autogen.forecast_config import EvaluationPeriodParam
  from greykite.framework.templates.autogen.forecast_config import ForecastConfig
  from greykite.framework.templates.autogen.forecast_config import MetadataParam
  from greykite.framework.templates.autogen.forecast_config import ModelComponentsParam
  from greykite.framework.templates.forecaster import Forecaster
  from greykite.framework.templates.model_templates import ModelTemplateEnum
  from greykite.framework.utils.framework_testing_utils import assert_basic_pipeline_equal
  from greykite.sklearn.estimator.auto_arima_estimator import AutoArimaEstimator
label_desc: Use image node_id 3 for calling a global function with example usage: test_auto_arima_hyperparameter_grid_default() without return types